Faillure Analysis for Domain Knowledge Acquisition in a Knowledge-Intensive CBR System

International audienceA knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should improve the accuracy of such a system. This paper presents an approach for knowledge acquisition based on some failures of the system. The CBR system is assumed to produce solutions that are consistent with the domain knowledge but that may be inconsistent with the expert knowledge, and this inconsistency constitutes a failure. Thanks to an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge. Another type of failures occurs when the solution produced by the system is only partial: some additional pieces of information are required to use it. Once again, an interaction with the expert involves the acquisition of new knowledge. This approach has been implemented in a prototype, called FrakaS, and tested in the application domain of breast cancer treatment decision support

Case-based Maintenance Model for handling Relevant and Irrelevant Cases in Case-based Reasoning System

Case-based maintenance can be resource intensive and requires significant time and effort to collect and analyse all cases. This can lead to inefficiencies and high costs in the entire case-based reasoning system. Accordingly, the Relative Coverage Condensed Nearest Neighbour had been created to reduce the number of cases in a dataset by selecting a subset of representative cases, whereas maintaining the overall performance of the whole system. Besides, Footprint utility deletion is a type of case deletion algorithm that can remove redundant or irrelevant cases from a storage, though maintaining the system’s competency. Recently, Hybrid approach was given to ensure that the case-base remains up-to-date and relevant, while also reducing its size and complexity. However, the results from using these approaches seem to be improved for the better performance. Therefore, the proposed model is developed, which comprises two main phrases by using case-based reasoning and identifying relevant and irrelevant cases to provide better results. The reduction size of case-base is lower than the traditional studies approximately 1-9% and also gives higher percentage of solving problems about 1-7%, while the average problem-solving time is shorter than them nearly at most 8 times. &nbsp

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system

Improving the Relevance of Cyber Incident Notification for Mission Assurance

Military organizations have embedded Information and Communication Technology (ICT) into their core mission processes as a means to increase operational efficiency, improve decision making quality, and shorten the kill chain. This dependence can place the mission at risk when the loss, corruption, or degradation of the confidentiality, integrity, and/or availability of a critical information resource occurs. Since the accuracy, conciseness, and timeliness of the information used in decision making processes dramatically impacts the quality of command decisions, and hence, the operational mission outcome; the recognition, quantification, and documentation of critical mission-information resource dependencies is essential for the organization to gain a true appreciation of its operational risk. This research identifies existing decision support systems and evaluates their capabilities as a means for capturing, maintaining and communicating mission-to-information resource dependency information in a timely and relevant manner to assure mission operations. This thesis answers the following research question: Which decision support technology is the best candidate for use in a cyber incident notification system to overcome limitations identified in the existing United States Air Force cyber incident notification process